Download Phasic Activation of Individual Neurons in the Locus Ceruleus

13656 • The Journal of Neuroscience, October 8, 2014 • 34(41):13656 –13669
Systems/Circuits
Phasic Activation of Individual Neurons in the Locus
Ceruleus/Subceruleus Complex of Monkeys Reflects
Rewarded Decisions to Go But Not Stop
Rishi M. Kalwani,1 X Siddhartha Joshi,2 and X Joshua I. Gold2
1Temple University School of Medicine, Philadelphia, Pennsylvania 19140, and 2Department of Neuroscience, University of Pennsylvania, Philadelphia,
Pennsylvania 19104
Neurons in the brainstem nucleus locus ceruleus (LC) often exhibit phasic activation in the context of simple sensory-motor tasks. The
functional role of this activation, which leads to the release of norepinephrine throughout the brain, is not yet understood in part because
the conditions under which it occurs remain in question. Early studies focused on the relationship of LC phasic activation to salient
sensory events, whereas more recent work has emphasized its timing relative to goal-directed behavioral responses, possibly representing the end of a sensory-motor decision process. To better understand the relationship between LC phasic activation and sensory, motor,
and decision processing, we recorded spiking activity of neurons in the LC⫹ (LC and the adjacent, norepinephrine-containing subceruleus nucleus) of monkeys performing a countermanding task. The task required the monkeys to occasionally withhold planned,
saccadic eye movements to a visual target. We found that many well isolated LC⫹ units responded to both the onset of the visual cue
instructing the monkey to initiate the saccade and again after saccade onset, even when it was initiated erroneously in the presence of a
stop signal. Many of these neurons did not respond to saccades made outside of the task context. In contrast, neither the appearance of the
stop signal nor the successful withholding of the saccade elicited an LC⫹ response. Therefore, LC⫹ phasic activation encodes sensory
and motor events related to decisions to execute, but not withhold, movements, implying a functional role in goal-directed actions, but
not necessarily more covert forms of processing.
Key words: countermanding; norepinephrine; saccade
Introduction
The locus ceruleus (LC) is the primary source of norepinephrine
(NE) transmitted to the rest of the brain (Jones and Moore, 1977;
Aston-Jones and Cohen, 2005). The LC–NE system contributes
to numerous brain functions, including arousal, sensory-motor
processing, attention, learning, and memory (Jouvet, 1969; Harley, 1987; Cahill and McGaugh, 1996; Berridge and Waterhouse,
2003; Aston-Jones and Cohen, 2005; Sara, 2009; Carter et al.,
2010; Howells et al., 2012; Sara and Bouret, 2012). These contributions involve, at least in part, phasic activation of the LC to
reward-predicting events, particularly when the events are surprising and thus lead to abrupt cognitive and behavioral shifts
(Aston-Jones and Bloom, 1981b; Devauges and Sara, 1990; Sara
and Segal, 1991; Aston-Jones et al., 1997; Bouret and Sara, 2004;
Received June 23, 2014; revised Aug. 5, 2014; accepted Aug. 8, 2014.
Author contributions: R.M.K., S.J., and J.I.G. designed research; R.M.K. and S.J. performed research; S.J. contributed unpublished reagents/analytic tools; R.M.K. and J.I.G. analyzed data; R.M.K., S.J., and J.I.G. wrote the paper.
This work was supported by the National Institutes of Health (Grants P50 MH062196, R21 MH093904, R01
EY015260, and P30 EY001583), the McKnight Endowment Fund for Neuroscience, the Burroughs-Wellcome Fund,
and the Sloan Foundation. We thank Jon Cohen, Long Ding, Takahiro Doi, Siddhartha Joshi, Yin Li, and Matt Nassar
for helpful comments.
The authors declare no competing financial interests.
Correspondence should be addressed to Joshua I. Gold, Department of Neuroscience, University of Pennsylvania,
116 Johnson Pavilion, Philadelphia, PA 19104-6074; E-mail: [email protected].
DOI:10.1523/JNEUROSCI.2566-14.2014
Copyright © 2014 the authors 0270-6474/14/3413656-14$15.00/0
Bouret and Sara, 2005; Corbetta et al., 2008). However, basic
questions about this phasic activation remain unanswered, including its relationship to sensory and motor events and the internal decision processes that link the two (Aston-Jones and
Bloom, 1981b; Rasmussen et al., 1986; Grant et al., 1988; HervéMinvielle and Sara, 1995; Usher et al., 1999; Bouret and Sara,
2004; Clayton et al., 2004; Aston-Jones and Cohen, 2005; Bouret
and Richmond, 2009). The goal of the present study was to clarify
these relationships by recording single-unit LC activation in
monkeys performing a countermanding task that involves decisions to execute and withhold visually guided saccadic eye movements (Fig. 1; Logan et al., 1984; Hanes and Schall, 1995; Hanes et
al., 1998; Hanes and Carpenter, 1999).
For the countermanding task, a visual “go” signal instructs the
subject to make a visually guided saccade to a peripheral target,
but an occasional visual “stop” signal instructs the subject to
withhold the impending saccade. The “go” process involves a
buildup of oculomotor activity toward a threshold (Carpenter
and Williams, 1995; Hanes and Schall, 1996). Less is known about
the “stop” process, but one well established set of models assumes
that this process involves a similar rise-to-threshold mechanism
that races against the go process (Logan and Cowan, 1984;
Boucher et al., 2007; Verbruggen and Logan, 2009). Alternatively,
the go process might simply be terminated by detection of the
stop signal (Salinas and Stanford, 2013). In either case, the
Kalwani et al. • Phasic Activation of Locus Ceruleus
A
B
Figure 1. Countermanding task. A, No-stop-signal trials. After the monkey fixated on a
central fixation point for a variable time (0.8 –1.1 s), a peripheral target appeared at 1 of 2
locations along the horizontal axis. The monkey was rewarded for making a saccade to the
target. B, Stop-signal trials. On 33% of randomly selected trials, after the target appeared and
after one of five stop-signal delays chosen at random, the fixation point reappeared, instructing
the monkey to withhold its planned saccade. The monkey was rewarded for maintaining fixation (correct trials). If the monkey failed to withhold its saccade, no reward was given (incorrect
trials).
timing of LC phasic activation can be analyzed relative to both
the sensory signals and the subsequent commitments to either
go or stop.
We present two novel findings that substantially clarify the
conditions that elicit phasic responses in the LC and the adjacent,
NE-containing subceruleus nucleus (subC; here, we refer to these
nuclei together as LC⫹; Sharma et al., 2010; Smeets and
González, 2000). First, we show that individual LC⫹ units can
exhibit phasic activation to both sensory and motor events occurring immediately in sequence, in contrast to previous reports that
typically emphasized one or the other response type, but not both
(Foote et al., 1980; Aston-Jones and Bloom, 1981b; Grant et al.,
1988; Hervé-Minvielle and Sara, 1995; Sara and Segal, 1991; Rajkowski et al., 2004; Aston-Jones et al., 1994; Clayton et al., 2004;
but see Bouret and Richmond, 2009, which reported sensoryand motor-locked responses, albeit more separated in time). Second, we show that LC⫹ neurons respond phasically to rewarded
decisions to go but not to stop, implying a motor-centric role that
does not necessarily generalize to more covert forms of decisions.
Materials and Methods
Subjects. Two adult male rhesus monkeys (Macaca mulatta) were used for
this study. All training, surgery, and experimental procedures were performed in accordance with the National Institutes of Health’s Guide for
the Care and Use of Laboratory Animals and were approved by the University of Pennsylvania Institutional Animal Care and Use Committee.
Behavioral task. The monkeys performed a countermanding task,
which included visually guided saccade trials occasionally interrupted
with a stop signal (Fig. 1). All trials began with the presentation of a
central fixation point, which was a red circle subtending 2° of visual
angle. After the monkey fixated for a variable time (0.8 –1.1 s, uniformly
distributed), the fixation point turned off and, simultaneously, a target (a
red circle subtending 3° of visual angle) appeared at 1 of 2 locations either
directly to the right or directly to the left at 8° eccentricity (the “go
signal”). Go-signal onset instructed the monkey to make a saccadic eye
movement to look directly at the target. On 33% of the trials, the fixation
point reappeared (the “stop signal”) after a delay known as the stopsignal delay (50, 100, 150, 200, or 250 ms). Stop-signal onset instructed
J. Neurosci., October 8, 2014 • 34(41):13656 –13669 • 13657
the monkey to withhold its saccade and maintain fixation. On stop-signal
trials, the fixation point and target stimulus remained on for 400 ms after
stop-signal onset. The monkey was rewarded with a drop of water or
Kool-Aid for maintaining fixation on the fixation point throughout this
time. On no-stop-signal trials, the monkey was rewarded for making a
saccade to the target within 700 ms of fixation point offset and maintaining gaze on the target for 400 ms.
Eye position was monitored using a video-based system (Eye-Link
2000; SR Research) sampled at 1000 Hz. Visual stimuli were presented on
an LCD monitor (Viewsonic) located 60 cm from the monkey’s eyes. The
monitor introduced systematic delays in the timing of the onset and
offset of visual stimuli relative to when it received a new frame from the
video card, as measured using a photodiode (OSI Optoelectronics). Because these delays remained constant across multiple measurements, we
adjusted measurements of LC⫹ response latencies using fixed offsets
equal to these delays (15 and 17 ms for fixation point onset and offset,
respectively).
Electrophysiology. Each monkey was implanted with a head holder and
recording cylinder that provided access to the LC⫹ (Monkey O had two
separate cylinders implanted at separate times, first targeting the left
LC⫹ and then targeting the right LC⫹; Monkey C had a single cylinder
targeting the left LC⫹). The cylinders were placed at Horsley–Clark coordinates 0 mm AP, 5 mm L, and tilted at an angle of 9° along the ML
plane to point downward toward the midline. We used several techniques to target LC⫹, including MRI to determine the trajectory of the
recording chamber relative to monkey’s brain (Kalwani et al., 2009);
physiological properties of neurons in the brain structures encountered
along recording trajectories leading to the LC⫹, which included the superior colliculus, inferior colliculus (IC), and the trochlear decussation in
the brainstem; characteristics of noradrenergic LC neurons reported in
previous studies (Aston-Jones et al., 1994; Bouret and Sara, 2004; Bouret
and Richmond, 2009), including waveform shape, sensitivity to arousing
external stimuli (e.g., door knocking, key shaking), and decreased firing
during observed periods of drowsiness (e.g., eyelid drooping, disengagement from behavioral tasks); and effects of intramuscular injection of the
␣2-noradrenergic agonist clonidine (Bouret and Richmond, 2009).
Neural recordings were conducted using custom-made quartz-coated
platinum-tungsten microelectrodes (Thomas Recording) and a Multichannel Acquisition System (Plexon). Spike waveforms were sorted offline. Single-unit isolation quality was determined by estimating both
false-positive (FP) and false-negative (FN) rates, as follows (for details,
see Hill et al., 2011). FP rate was computed as the maximum of 2 values:
(1) the expected rate of refractory period violations based on a refractory
period of 2.6 ms (Segal et al., 1983) and (2) the amount of overlap
between an identified signal and noise distribution each fit to a multivariate Gaussian. FN rate was computed as (1 ⫺ (1 ⫺ FN1) (1 ⫺ FN3) ⫹
FN2, where FN1 estimated false negatives based on a high threshold for
spike detection, FN2 was computed from the amount of overlap between
a signal and noise distribution each fit to a multivariate Gaussian, and
FN3 represented “censored” events based on the time-out period of 1.4
ms after waveform triggering.
Behavioral data analysis. Reaction time (RT) was measured as the time
between stimulus onset and saccade onset. Saccades were detected by
analyzing acceleration data using an adaptive-threshold model (Behrens
et al., 2010). Behavior on stop-signal trials was quantified using inhibition functions, which describe the probability of making an error as a
function of stop-signal delay (Hanes and Schall, 1995). These functions
were fit with a cumulative Weibull function of the following form:
W 共 t 兲 ⫽ ␥ ⫹ 共 1 ⫺ ␥ ⫺ ␭ 兲 ⴱ 共 1 ⫺ exp共 ⫺ 共t/␣兲␤ 兲兲
where t is the stop-signal delay, ␣ is the stop-signal time at which the
inhibition function reaches 80% of its growth, ␤ is the slope, ␥ is the
lower asymptote, and ␭ is the upper asymptote.
Unlike for RT on “go” trials, there is no overt way to assess when the
monkey has stopped. Instead, the time at which the monkey stops,
known as the stop-signal reaction time (SSRT), must be inferred using
the RT distribution and the inhibition function (Logan and Cowan,
1984; Hanes and Schall, 1995). The SSRT was derived by matching the
Kalwani et al. • Phasic Activation of Locus Ceruleus
13658 • J. Neurosci., October 8, 2014 • 34(41):13656 –13669
probability of error for a given stop-signal delay from the inhibition
function to the same probability on the cumulative RT function from
no-stop-signal trials. The SSRT is the difference between the time associated with this probability on the cumulative RT function and the stopsignal delay.
Neural data analysis. Phasic neural activation was quantified as the
spike rate measured in a 100-ms-wide window, defined separately for
each unit relative to sensory or motor events. For sensory activation, the
100 ms response window had a start time between 50 and 100 ms after
go-signal onset, chosen to provide the maximum spike rate from that
interval. For motor activation, the 100 ms response window had a start
time between 20 ms preceding and 30 ms after saccade onset, chosen to
provide the maximum spike rate from that interval. For analyses that
compared sensory and motor responses (e.g., Fig. 7), we used only trials
with RT ⬎ 220 to ensure that the sensory and motor response windows
did not overlap. Because spike rates tended to be very low (⬍10 spikes/s),
responses in these 100 ms time windows typically consisted of either zero
or one spike. We therefore used Fisher’s exact test to compare responses
between different windows or different task conditions for a given
window.
Histology. After transcardial perfusion with 10% buffered formalin
(Fisher Scientific), the brain was removed and allowed to postfix in formalin at 4°C for a week. It was then blocked in the coronal plane and
resectioned at 60 ␮m in 0.1 M phosphate buffer (PB) using a Leica vibratome. Sections were collected in 0.1 M PB and rinsed twice (10 min, 30
min) in the same solution and then three times (10 min each) in 0.01 M
PB. Alternate sections were stained using standard protocols for the Nissl
procedure (on slides) and immunostaining for tyrosine hydroxylase
(floating sections; primary antibody: anti-TH rabbit, AB152; Millipore).
The remaining materials and methods were similar to those described
previously (Sharma et al., 2010). A set of control sections was also processed without the primary antibody and found to show no labeling. All
slides were dehydrated (in alcohol), defatted (in xylene), and then coverslipped using Permount (Fisher Scientific). Sections were imaged using
a Leica microscope fitted with a digital camera. Montages of multiple
sections for the final figures were prepared using the Gnu Image Manipulation Program (GIMP).
Results
The goal of this study was to characterize the activity of individual
neurons in the LC⫹ of two monkeys performing a countermanding task (Fig. 1; Logan et al., 1984; Hanes and Schall, 1995; Hanes
et al., 1998; Hanes and Carpenter, 1999). For this task, the monkeys were rewarded for making visually guided saccades to a peripheral target, but the saccade was withheld when a stop signal
was occasionally presented after target onset (33% of randomly
interleaved trials). The targets always appeared in one of two
locations along the horizontal axis, directly right or left of fixation. We varied the time between the presentation of the go signal
(simultaneous offset of the fixation point and onset of the peripheral target) and the stop signal (reappearance of the fixation
point), referred to as the stop-signal delay. The task allowed us to
characterize LC⫹ activation with respect to the visual “go” and
“stop” signals, the decision to go and subsequent saccadic response, the decision to stop, and task-irrelevant movements. In
the following sections, we first describe behavioral performance
on the countermanding task, then detail how we targeted LC⫹
units, and finally describe the response properties of these units
in the context of task performance.
Task performance
The performance of both monkeys on the countermanding task
was consistent with results from previous studies (cf. Fig. 2 A, C,
with Figures 2 and 3, respectively, in Emeric et al., 2007). On
no-stop-signal trials, the monkeys had median [interquartile
range or IQR] RTs of 267 [233 311] ms for Monkey O and 253
A
B
C
Figure 2. Behavior for Monkeys O (black) and C (gray). A, Inhibition functions describing the
probability of noncancelled saccades on stop-signal trials (errors) as a function of stop-signal
delay. Points and error bars (mostly obscured by points) are mean ⫾ SEM measured across
sessions. Lines are fits of Equation 1 to data from all sessions for each monkey. B, RT distributions. Box and whiskers correspond to 25th/75th percentiles and first/99th percentiles, respectively; center line indicates the median value. Filled boxes correspond to RTs on noncancelled or
no-stop-signal trials; that is, trials with an overt motor response. Open boxes correspond to
SSRTs (see Materials and Methods) on cancelled stop-signal trials. Black and gray symbols
correspond to the two monkeys, as in A. C, Sequential trial effects on RT. The first column shows
the mean RT on all no-stop-signal trials (also shown as the horizontal dashed line). The second
column shows the mean RT on all noncancelled saccades on stop-signal trials. All other columns
show the mean RT on no-stop-signal trials after the sequence of trials indicated on the abscissa.
[213 293] ms for Monkey C (Fig. 2B). On stop-signal trials, their
ability to cancel a saccade decreased systematically with increasing stop-signal delay (Fig. 2A; Logan et al., 1984; Hanes and
Schall, 1995; Hanes et al., 1998; Hanes and Carpenter, 1999).
Their RTs on noncancelled (incorrect) stop-signal trials tended
to increase with increasing stop-signal delay, reflecting more time
to prepare and execute the saccadic response (Fig. 2B). SSRTs
computed for each stop-signal delay using the RT distribution
from no-stop-signal trials (Logan and Cowan, 1984) tended to
decrease slightly as a function of stop-signal delay (Fig. 2B). RTs
on no-stop-signal trials were also influenced by the recent trial
history, including a tendency for shorter RTs after no-stop-signal
trials and longer RTs after stop-signal trials (Fig. 2C; Rieger and
Gauggel, 1999; Emeric et al., 2007; Verbruggen and Logan, 2008;
Nelson et al., 2010; Pouget et al., 2011).
Kalwani et al. • Phasic Activation of Locus Ceruleus
J. Neurosci., October 8, 2014 • 34(41):13656 –13669 • 13659
Figure 3. Targeting LC⫹. A–C, Magnetic resonance images from axial (A), sagittal (B), and coronal (C) planes showing the approximate location of the LC⫹ (spot added at the center of the
crosshairs) in the brain of Monkey C based on nearby landmarks including the IC (labeled in B) and fourth ventricle (labeled in C). Recording microelectrodes were advanced dorsal-to-ventral and
slightly lateral-to-medial along the long axis of a circular recording cylinder placed on the skull. A virtual, 3D projection of the recording cylinder through the brain is shown as dashed lines (Kalwani
et al., 2009). Images are shown using AFNI (Cox, 1996). D, Schematic of a coronal section of the macaque brain showing structures typically encountered in an electrode track leading to the pontine
brainstem, as detailed in E–H. (Adapted from Paxinos et al., 2008, Plate 90, Interaural 0.3, bregma ⫺21.60). E, Superior colliculus. Single-unit spatial tuning on a visually guided saccade task
(responses aligned to onset of saccades in the direction of the given axes relative to central fixation) and microstimulation-evoked saccades (inset at center) were to the right and slightly downward,
placing the recording site at a lateral-rostral location in intermediate layer (Robinson, 1972; Sparks and Nelson, 1987). Median [IQR] interspike interval (ISI) from 42 sites recorded in our
laboratory ⫽ 30 [14 67] ms. F, IC. Raster and PSTH of a multiunit recording aligned to an unexpected auditory beep, which evoked a strong and reliable response. Median [IQR] ISI from
130 sites recorded in our laboratory ⫽ 21 [8 55] ms. G, Trochlear decussation. Multiunit activity during a visually guided saccade task, plotted as in E. This site had a characteristic
ramp-and-hold response for saccades to locations in the lower hemifield (Henn et al., 1982). Median [IQR] ISI from 10 sites recorded in our laboratory ⫽ 11 [7 19] ms. H, LC⫹ multiunit
recording. Raster and PSTH of a multiunit recording aligned to an unexpected auditory beep, which evoked a phasic response followed by a brief suppression of tonic activity. Median [IQR]
ISI from 112 sites recorded in our laboratory ⫽ 53 [20 157] ms.
13660 • J. Neurosci., October 8, 2014 • 34(41):13656 –13669
Kalwani et al. • Phasic Activation of Locus Ceruleus
Targeting LCⴙ
A
B
C
We used several techniques to target LC⫹
neurons for recording. Here, we present
these techniques in detail to support our
contentions that: (1) our neural data were
from LC⫹ and (2) for this dataset, we
were unable to distinguish LC from subC
(Paxinos et al., 2008; Sharma et al., 2010).
First, we used MRI to estimate LC⫹ location based on several local landmarks, including the IC and the fourth ventricle
Figure 4. Clonidine effects. A–C, Systemic injection of clonidine (arrows) reduced LC⫹ activity at individual sites in the
(Fig. 3A–C). Second, during recording left hemisphere of Monkey C (A), the left hemisphere of Monkey O (B), and the right hemisphere of Monkey O (C). Dashed
sessions, we verified appropriate penetra- lines indicate 6 min running means of spike rates. The monkeys were noticeably drowsy (eyes closed) during the times
tion trajectories using stereotyped pat- indicated by the horizontal black bars.
terns of responses along our electrode
penetrations through the superior collicuend up in structures with LC⫹ properties also did not encounter
lus, IC, and brainstem structures, including the trochlear dethe stereotypical patterns of responses through other structures
cussation (Fig. 3D–H ). In the superior colliculus, we paid
(e.g., superior colliculus, IC, and brainstem structures) that we
particular attention to trajectories of saccades evoked with elecreliably found when targeting LC⫹. Together, these findings sugtrical microstimulation, which we used to map our location
gest that we sampled both LC and subC in our recordings (n ⫽ 69
within this nucleus (Robinson, 1972). Third, we identified LC⫹
distinct LC⫹ sites, consisting of 43 and four from the left and
neurons based on previously reported characteristics (Astonright hemisphere, respectively, of Monkey O, and 22 from the left
Jones et al., 1994; Bouret and Sara, 2004; Bouret and Richmond,
hemisphere of Monkey C). Because we did not identify any sys2009): broad, biphasic waveforms (for all recorded units, the time
tematic differences in the criteria used to identify recording sites
from the negative to the positive peak of the average waveform
for the two monkeys, for all subsequent analyses, we present their
had a median [IQR] value of 488 [413 575] ␮s for Monkey O and
data combined together.
450 [375 500] ␮s for Monkey C; by comparison, the average
We analyzed two sets of neural (spiking) data that were obwaveforms of 10 representative single units in the IC had widths
tained from the same raw recording signals in individual sessions
of 390 [220 412] ␮s, and 10 in the SC of 245 [175 315] ␮s); low
but differed in terms of single-unit isolation quality. For one set,
spontaneous firing rate (during an 800 ms epoch just before fixwhich we refer to as “single-unit data,” we used conservative
ation point offset on the visually guided saccade task, there were
spike-sorting criteria to isolate 64 single units (one pair was iso0 –2 spikes recorded on 78.7% of all trials at all sites in Monkey O
lated on a single electrode; the remainder were isolated alone),
and 92.7% in Monkey C); ⬍4.4 mm distance from IC along the
thereby allowing us to characterize how single LC⫹ neurons endorsoventral electrode trajectories (median [IQR] ⫽ 1.80 [1.37
code multiple aspects of task performance. Figure 6A shows esti2.38] mm for Monkey O and 2.25 [1.43 3.01] mm for Monkey C);
mates of false-positive and false-negative rates for these units
sensitivity to arousing external stimuli (e.g., door knocking, key
(Hill et al., 2011). The false-positive rates had median [IQR]
shaking); and decreased firing during observed periods of drowsvalues of 0.01 [0.00 0.06], implying that it was unlikely that the
iness (e.g., eyelid drooping, disengagement from behavioral
analyses of the response properties of these units were corrupted
tasks). Fourth, three units were pharmacologically confirmed as
by contributions of other units. However, in these cases, we also
consistent with LC⫹ physiology: a 5 ␮g intramuscular injection
likely underestimated firing rates, because false-negative rates
of the ␣2-noradrenergic agonist clonidine, which is one-quarter
were higher (0.23 [0.06 0.90], paired test for H0: equal medians of
of the dose delivered in previous studies (Bouret and Richmond,
false-positive and false-negative rates, p ⬍ 0.01).
2009), led to a lower baseline firing rate and concurrent drowsiThe second dataset, which we refer to as “multiunit data,” inness (Fig. 4).
cluded as spikes all waveforms that crossed a threshold defined durThe above techniques have been used to identify units in LC
ing the recording session for each of the 69 recording sites. These
but not, to our knowledge, in subC of nonhuman primates
data thus likely included the activity from several local neurons, pos(Aston-Jones et al., 1994; Bouret and Sara, 2004; Bouret and
sibly including those not in the LC⫹. These multiunit signals yielded
Richmond, 2009). The subC is located just adjacent to the LC and
spike rates that were 1.0 –52.3 times greater than the corresponding
therefore could have easily been targeted by our electrode trajecsingle-unit signals recorded at the same time (Fig. 6B).
tories (Paxinos et al., 2008; Sharma et al., 2010). After our recordNo task-related tonic LCⴙ activation
ing experiments, we examined these trajectories in more detail
The LC exhibits at least two distinguishable modes of activation
using histological reconstructions from one of the monkeys
in animals performing simple sensory-motor tasks (Aston-Jones
(Monkey O). As shown in Figure 5, we believe that at least some
of our trajectories are most consistent with targets in the subC,
and Bloom, 1981a; Aston-Jones and Cohen, 2005). In tonic
mode, ongoing activity that is either too low or too high can
not the LC. In Monkey C, we subsequently did more precise
reflect drowsiness or distractibility, respectively, with intermedielectrophysiological mappings around putative LC⫹ units and
ate levels necessary for attentive engagement in goal-directed befound similar waveform shapes and response properties (i.e., all
havior (Aston-Jones et al., 1999; Usher et al., 1999). In phasic
conforming to our LC⫹ identification criteria described in the
mode, short bursts of action potentials occur in relation to taskprevious paragraph) separated by up to ⬃1 mm in brain-surface
relevant events. The phasic activation is strongest with intermecoordinates along a medial-lateral axis and up to ⬃3 mm along
diate levels of background tonic activity.
the rostral-caudal axis, which presumably would correspond to
We found no evidence for task-related modulation of tonic
both LC and subC (Paxinos et al., 2008). These mapping experiments also confirmed that nearby electrode tracts that did not
activity. We measured LC⫹ tonic activity for each trial as the
Kalwani et al. • Phasic Activation of Locus Ceruleus
J. Neurosci., October 8, 2014 • 34(41):13656 –13669 • 13661
Figure 5. Histology from the right hemisphere of Monkey O. A, Nissl staining used to identify brain regions at the level of the pontine brainstem. Arrow shows a small section of track and lesion
visible just ventral to the superior cerebellar peduncle (scp). The track leads to subC. B, Schematic showing brainstem regions that were identified in A and C. (Adapted from Paxinos et al., 2008, Plate
90, Interaural 0.3 mm, bregma: ⫺21.60 mm). C, Tyrosine hydroxylase (TH) immunohistochemistry allowed us to identify the LC and subCV, the only two regions with neuronal staining. This section
was adjacent to the one shown in A. D, E, Electrode tracks (indicated by arrows) in Nissl-stained sections adjacent to the one shown in A. F, Upper four panels show higher-magnification images of
the region within the larger bounding box in C, which includes neuronal staining localized to LC. Bottom two panels show TH-immunostained axons in cortex. 4V indicates fourth ventricle; 4x,
trochlear decussation; Cb/Cb2, cerebellum; LV, lateral ventricle; mcp, middle cerebellar peduncle; me5, trigeminal mesencephalic nucleus; mlf, medial longitudinal fasciculus; Pn, pontine nuclei; scp,
superior cerebellar peduncle; subCD/subCV, dorsal/ventral parts of the subceruleus; and vsc, ventral spinocerebellar tract.
spike rate during the final 800 ms before fixation point offset. The
mean spike rate during this epoch was the same on correct and
incorrect-stop-signal trials (paired t test for H0: equal means per
session, p ⫽ 0.50 for single-unit data, 0.16 for multiunit data) and
was not related to RT on no-stop-signal trials (mean ⫾ SEM
correlation coefficient across sessions ⫽ ⫺0.01 ⫾ 0.01, t test for
H0: mean ⫽ 0, p ⫽ 0.33 for single-unit data; ⫺0.02 ⫾ 0.01, p ⫽
0.11 for multiunit data) or on incorrect-stop-signal trials (correlation coefficient ⫽ ⫺0.01 ⫾ 0.02, p ⫽ 0.74 for single-unit data;
0.00 ⫾ 0.02, p ⫽ 0.99 for multiunit data). These results reflected
a relatively constant, low-level of LC⫹ tonic activation evident
throughout all of our recording sessions (median [IQR] tonic
rate across all neurons and all trials ⫽ 1.25 [0 2.50] spikes/s for
single-unit activity and 5.00 [1.25 12.00] spikes/s for multiunit
data) when the monkeys were consistently engaged with the task.
Therefore, subsequent analyses focus on LC⫹ phasic activation.
Task-related phasic LCⴙ activation
Previous studies have variously emphasized either sensory or motor aspects of LC phasic activation (Berridge and Waterhouse,
2003; Clayton et al., 2004). We found that many LC⫹ units provided distinct, phasic responses to both sensory and motor
13662 • J. Neurosci., October 8, 2014 • 34(41):13656 –13669
Kalwani et al. • Phasic Activation of Locus Ceruleus
events. An example single unit from
B
A
Monkey O is shown in Figure 7. This
unit had a composite false-positive rate
of 0.014 and a composite false-negative
rate of 0.058. For all trial types, the “go”
signal (simultaneous offset of the fixation point and onset of the peripheral
target) evoked a phasic activation that
peaked at a latency of ⬃130 ms. A second, distinct phasic activation occurred
⬃40 ms after the onset of the saccadic
response on no-stop-signal and
incorrect-stop-signal trials. However,
no activation was seen on correct-stopsignal trials around the time of the
SSRT, which is the time that the monkey
commits to withholding the response
and is inferred from the RT distribution Figure 6. Unit characteristics. A, Quantification of the isolation quality of single-unit recordings in LC⫹. Composite falseon no-stop-signal trials and the inhibi- positive rate is the estimated fraction of waveforms identified as belonging to the given unit (points) that likely belong to a
tion function (Logan and Cowan, 1984; different unit or noise. Composite false-negative rate is the estimated fraction of waveforms identified as not belonging to the
given unit that likely do belong to the given unit. False-negative rates ⬎1 are truncated at 1. For details, see Materials and Methods
Hanes and Schall, 1995). Therefore, this and Hill et al. (2011). B, Mean spike counts across all trials from individual recordings sites for multiunit versus single-unit isolation.
unit encoded the go signal and the decision to go, but not the decision to stop.
The sensory and motor responses were evident together in
The population of 64 recorded single LC⫹ units had a similar
individual trials. Because overall spike rates were so low, a “recombination of sensory and motor responses (Fig. 8). On aversponse” in the given 100 ms epoch typically consisted of only a
age, the largest phasic activation occurred just after the onset of
single spike. Figure 9C shows, for each single unit, the probability
the visually guided saccade on both no-stop-signal and incorrectof a spike occurring in the motor epoch plotted as a function of
stop-signal trials. There was also a slightly smaller phasic activathe probability of a spike occurring in the motor epoch given that
tion that occurred ⬃100 –200 ms after the onset of the go signal
a spike had already occurred in the immediately preceding senon all trial types. There was no apparent activation after this
sory epoch from the same trial. This analysis used only trials with
sensory-driven activation on correct-stop-signal trials, when the
RT ⬎ 220 to ensure that the sensory and motor response epochs
monkey withheld its planned saccade. In the following sections,
did
not overlap. The data cluster around the main diagonal, inwe analyze these sensory- and motor-related phasic activations in
dicating
similar values for a given unit (paired t test for H0: mean
more detail.
difference ⫽ 0, p ⫽ 0.61). Therefore, individual LC⫹ units can
consistently provide both sensory- and motor-related phasic acSingle and multiunit activation to both sensory and
tivation in the context of a simple visually guided saccade task.
motor events
Our conservative spike-sorting criteria for single units did
On no-stop-signal trials, the sensory “go” signal and the subsenot
result in missed features of the neural responses. Figure 9,
quent saccadic response were both associated with time-locked
D–F, shows the same analyses as in Figure 9, A–C, but using
phasic LC⫹ activation (Fig. 9). Figure 9A shows average
the neural recordings sorted as multiunit data. Other than
perievent time histograms (PETHs) of data from all single units,
substantially higher firing rates, the multiunit data showed the
aligned to either the go signal (gray) to show sensory-related
same trends as the single-unit data, including a combination
activation or the onset of the saccadic response (black) to show
of sensory responses and slightly larger motor responses from
motor-related activation. Qualitatively, this average motor response
individual trials and recording sites. The similarity between
was about twice as large as the average sensory response. Both were
single and multiunit responses were apparent in all subserelatively brief, with the motor response peaking 0 –100 ms after
quent analyses, so for the sake of brevity, only single-unit data
saccade onset and the sensory response peaking 100 –200 ms after
are shown in subsequent figures.
go-signal onset.
Only a small number of units exhibited spatial selectivity for
There was a similar combination of sensory and motor reeither
sensory- or motor-related responses. Spatial tuning was
sponses at most individual recording sites. Of the 64 single units,
measured
coarsely by comparing responses on correct no-stop39 had responses time locked to go-signal onset that were greater
signal trials when the target was either to the left (ipsilateral to the
than baseline (Fisher’s exact test for H0: equal probabilities of a
recording site in both monkeys) or right (contralateral) of fixaspike occurring in the baseline interval and of a spike occurring in
tion. For sensory responses, spatial selectivity was significant for
the sensory interval, p ⬍ 0.01; one had responses lower than
six units showing a contralateral bias and for one unit showing an
baseline; see Materials and Methods for details); 56 had responses
ipsilateral bias (Fisher’s exact test comparing the presence/abtime locked to saccade onset that were greater than baseline ( p ⬍
sence of a spike in the sensory epoch for the contralateral versus
0.01; two had responses lower than baseline); and 35 had sensoryipsilateral target location, p ⬍ 0.01). The population as a whole
and motor-related responses (using nonoverlapping epochs) that
did not show a bias (median [IQR] difference for contralateral
were both greater than baseline ( p ⬍ 0.01; Fig. 9B). The mean
versus ipsilateral targets ⫽ 0.13 [⫺0.33 1.01] spikes/s across all
motor response was larger than the mean sensory response from
single-unit data; paired Mann–Whitney test for H0: median difindividual units, corresponding to a median [IQR] ratio of 1.63
ference ⫽ 0, p ⫽ 0.44; Fig. 10A). For motor responses, spatial
[0.88 3.39] (Mann–Whitney test for H0: median ratio ⫽ 1, p ⬍
selectivity was significant for seven units showing a contralateral
0.01).
Kalwani et al. • Phasic Activation of Locus Ceruleus
J. Neurosci., October 8, 2014 • 34(41):13656 –13669 • 13663
eral sensory and motor responses across units ⫽ ⫺0.11, p ⫽ 0.37
for H0: correlation coefficient ⫽ 0).
We were not able to identify a reliable trial-by-trial relationship between the probability of a sensory- or motorrelated response and RT within individual sessions: of the 64
single units, the median RT was not different (Mann–Whitney
test, p ⬎ 0.01) on no-stop-signal trials without and with a
spike in the sensory epoch for 61 units (for the remaining three
units, two had slightly shorter RTs and one had slightly longer
RTs on trials without an LC⫹ sensory spike) and in the motor
epoch for 61 units (for the remaining three units, one had
slightly shorter RTs and two had slightly longer RTs on trials
without an LC⫹ sensory spike).
Figure 7. Single-unit example. A–D, Isolation quality. A, Threshold-triggered waveforms
recorded from a single electrode that were (black) and were not (gray) classified as the given
unit. B, Scatterplot of the first two principal components of each waveform shown in A. C,
Histogram of interspike intervals. Intervals smaller than the estimated LC refractory period of
2.6 ms (vertical dashed line, from Segal et al., 1983) are assumed to represent false-positive
categorizations. D, Histogram of the amplitude of the spike trough, normalized relative to the
threshold for detecting waveforms. The missing right tail of the Gaussian fit (gray curve) is
assumed to represent false-negative categorizations. E–G, Raster (top panel of each vertical
pair) and PETHs (computed as the mean and SD firing rate in 20-ms-wide bins, stepped in 1 ms
increments, across all trials; bottom) of activity from no-stop-signal (E), correct-stop-signal (F ),
and incorrect-stop-signal (G) trials, aligned to go-cue onset (left) and decision commitment
(right, go-cue onset plus RT for no-stop-signal and incorrect-stop-signal trials, go-cue onset
plus SSRT for correct-stop-signal trials). In E and G, rasters are sorted by RT. In F, rasters are
sorted by SSRT. Green markers indicate go-cue onset, blue markers indicate saccade onset, and
red markers indicate stop-cue onset.
bias and for one unit showing an ipsliateral bias ( p ⬍ 0.01). The
population as a whole showed a slight contralateral bias (median
[IRQ] difference ⫽ 0.35 [⫺0.27 1.13] spikes/s, p ⫽ 0.02). There
was no systematic relationship between the direction and magnitude of spatial tuning for sensory versus motor responses (Fig.
10C; Pearson’s correlation coefficient for contralateral–ipsilat-
Task-relevant and task-irrelevant motor responses
Previous studies have shown that motor-related phasic responses in LC⫹ are found in association with task-relevant
movements, but not in association with the same movements
when they are made outside of the task context (e.g., in the intertrial
interval; Yamamoto and Ozawa, 1989; Clayton et al., 2004). Our
results were only partially consistent with these findings, with some
individual LC⫹ neurons showing only task-relevant motor activation and others showing both task-relevant and task-irrelevant motor activation.
Figure 11A shows PETHs for all of the single-unit data under
two conditions. The first condition, identical to that shown in
Figure 9A, corresponded to responses time locked to onset of the
visually guided saccade on no-stop-signal trials. The second condition corresponded to responses time locked to the subsequent
saccade (i.e., release from fixation on the visual target after reward delivery; data were similar when considering other saccades
made in the intertrial interval). In both cases, there was a clear
peak in the PETH ⬃50 ms after saccade onset. However, the
magnitude of the peak response was substantially larger for the
first versus the second saccade.
The responses to task-relevant versus task-irrelevant saccades
for individual, well-isolated LC⫹ units are summarized in Figure
11B. Of the 64 units, 58 had task-relevant motor responses that
were greater than baseline (Fisher’s exact test, p ⬍ 0.01) and 23
had task-irrelevant motor responses that were greater than baseline ( p ⬍ 0.01; 22 of these had task-relevant responses). Of the 22
units with both task-relevant and task-irrelevant motor responses, eight had greater task-relevant responses, two had
greater task-irrelevant responses, and the remaining 12 units had
responses that were indistinguishable under the two conditions
(Fisher’s exact test, p ⬎ 0.01). Both kinds of responses were found
on individual trials, with the probability of a spike around the
time of the task-irrelevant saccade unaffected by the presence of a
spike around the time of the task-relevant saccade (Fig. 11C).
Therefore, across the population, task-relevant motor responses
were much more prevalent than task-irrelevant motor responses,
although task-irrelevant motor responses were found reliably in a
subset of well-isolated LC⫹ units.
There was no apparent systematic relationship between the
relative strength of task-relevant and task-irrelevant motor
responses and the relative strength of task-relevant sensory
and motor responses across individual units. We defined a
“motor-relevance index” as the difference between the taskrelevant and task-irrelevant motor response for a given unit
divided by their sum. The value of this index varied from ⫺1,
indicating strongly task-irrelevant responses, to 1, indicating
strongly task-relevant responses. After the results shown in
Kalwani et al. • Phasic Activation of Locus Ceruleus
13664 • J. Neurosci., October 8, 2014 • 34(41):13656 –13669
Figure 8. Pseudocolor plot of the mean response (see color scale to the right) from the full population of recorded LC⫹ units using trials sorted and binned by RT (black curve in A and B) or SSRT
(black curve in C) plotted as a function of time from go-cue onset. A, No-stop-signal trials. B, Incorrect-stop-signal trials. C, Correct-stop-signal trials.
A
B
C
D
E
F
Figure 9. Summary of sensory- and motor-related phasic activations on no-stop-signal trials. A, PETHs of LC⫹ activation from all single units (ribbons represent mean ⫾ SEM of the mean PETHs
from individual units) aligned to go-cue onset (gray) or saccade onset (black). B, Magnitude of the motor- versus sensory-aligned activation measured for individual units. Points and error bars are
mean ⫾ SEM. Solid points indicate that the responses from the two epochs were significantly different from each other (Fisher’s exact test, p ⬍ 0.01). C, The probability of getting a spike in the motor
epoch given that a spike occurred on the same trial in the sensory epoch plotted as a function of the overall probability of getting a spike in the motor epoch. Points represent data from individual
units. D–F are the same as A–C, except using multiunit data.
Figure 11B, the value of this index tended to be above zero,
indicating stronger task-relevant responses (median [IQR]
value ⫽ 0.21 [0.06 0.52]). We also defined a “sensory-motor
index” as the difference between the sensory response and the
task-relevant motor response for a given unit divided by their
sum. The value of this index varied from ⫺1, indicating
strongly sensory-driven responses, to 1, indicating strongly
motor-driven responses. After the results shown in Figure 9B,
the value of this index tended to be above zero, indicating
stronger motor-related responses (median [IQR] value ⫽ 0.18
[0.04 0.40]). The values of these indices were not correlated
across units (H0: Spearman’s correlation coefficient ⫽ 0, p ⫽
0.11). Instead, units strongly selective for task relevancy exhibited a range of selectivities for sensory/motor events and
units strongly selective for sensory/motor events exhibited a
range of selectivities for task relevancy.
We were not able to measure any topographic organization of
either index. The LC⫹ is too small and deep in the brain to allow
us to estimate the topography of our recording sites using our
recording methods.
Kalwani et al. • Phasic Activation of Locus Ceruleus
A
J. Neurosci., October 8, 2014 • 34(41):13656 –13669 • 13665
B
C
Figure 10. Weak spatial selectivity of LC⫹ phasic activation. A, Sensory-related spatial selectivity. Scatterplot of the mean response of individual LC⫹ units to go-cue onset when the visual
target was presented to the left (ipsliateral to the recording sites) or right (contralateral) of fixation. Solid points indicate that the responses were significantly different for the two target locations
(Fisher’s exact test, p ⬍ 0.01). B, Motor-related selectivity. Same conventions as A, but using responses aligned to saccade onset. C, Sensory- versus motor-related selectivity. The difference in
responses for contralateral versus ipsilateral targets were computed separately for sensory and motor responses of each unit.
A
B
C
Figure 11. Task-relevant versus task-irrelevant motor activation. A, PETHs of LC⫹ activation from all units (ribbons represent mean ⫾ SEM of the mean PETHs from individual units) aligned to
saccade onset for task-relevant (black) versus task-irrelevant (gray) saccades. B, Magnitude of the task-irrelevant versus task-relevant activity from individual units. Points and error bars are mean ⫾
SEM. Solid points indicate that the responses for the two conditions were significantly different from each other (Fisher’s exact test, p ⬍ 0.01). C, The probability of getting a spike in the
task-irrelevant saccade epoch given that a spike occurred in the immediately preceding task-relevant saccade epoch plotted as a function of the overall probability of getting a spike in the
task-irrelevant saccade epoch. Points represent data from individual units.
LCⴙ phasic responses on “go” decisions but not
“stop” decisions
A previous study reported phasic LC activation around the time
of a rewarded motor response to a visual target, but no activation
around the time of a correct but unrewarded decision to withhold
the motor response to a nontarget visual stimulus (Rajkowski et
al., 2004). In principle, that finding could reflect LC selectivity for
either a visually guided decision to execute versus withhold a
movement or the different reward contingencies. By rewarding
both correctly executed and correctly withheld movements, the
countermanding task allowed us to disentangle these possibilities
and test directly how LC⫹ phasic activation relates to visually
guided, rewarded decisions to go or stop.
We found clear differences between LC⫹ phasic activation on
trials with executed versus withheld movements. Summary
PETHs from all single units for no-stop-signal trials, incorrectstop-signal trials, and correct-stop-signal trials are shown in Figure 12A. The data from no-stop-signal trials (which were
rewarded) and incorrect-stop-signal trials (which were not rewarded) both show strong phasic activation peaking just after the
onset of the saccadic response. Within the first 100 ms after saccade onset, there was no difference in response magnitude for
these two trial types in 61 of the 64 recorded single units (Fisher’s
exact test, p ⬎ 0.01; Fig. 12B). In the subsequent 100 ms interval
(100 –200 ms after saccade onset), there were slightly fewer (59)
units with equivalent responses for no-stop-signal and incorrect-
stop-signal trials (Fig. 12C), reflected in a slightly truncated response for incorrect-stop-signal trials evident in the population
PETH (Fig. 12A).
In contrast, there was no apparent response on correct-stopsignal trials around the time of commitment to the stop process
(i.e., at the SSRT; Fig. 12 A, D,E). The majority of units had responses that did not differ from baseline (Fisher’s exact test, p ⬍
0.01) either during the first 100 ms (53/64 single units) or the
second 200 ms (59/64) after stop commitment. In both epochs,
this lack of responsiveness differed substantially from the gorelated responses on no-stop-signal trials (Fig. 12 D, E).
Moreover, the visual presentation of the stop signal did not
cause a reliable LC⫹ phasic activation. On incorrect-stop-signal
trials, any activation in response to stop-signal onset would likely
occur around the time of saccade onset, making it difficult to
distinguish sensory- and motor-related activations. Nevertheless,
the fact that most LC⫹ units had slightly weaker activation
within the first 100 ms of saccade onset for incorrect-stop-signal
trials (when the stop signal was present) versus no-stop-signal
trials (when the stop signal was absent) implies a lack of responsiveness to the presentation of the stop signal (Fig. 12B).
More direct evidence comes from correct-stop-signal trials,
which did not have motor-related activation after stop-signal
onset. Figure 13 shows PETHs of spiking data from all recorded
single units on correct-stop-signal trials. When the data are
aligned to go-signal onset (gray PETHs), there is a clear activation
Kalwani et al. • Phasic Activation of Locus Ceruleus
13666 • J. Neurosci., October 8, 2014 • 34(41):13656 –13669
A
A
B
B
C
D
D
Figure 12. LC⫹ activation with respect to decisions to go or stop. A, PETHs of LC⫹ activation from all units (ribbons represent mean ⫾ SEM of the mean PETHs from individual units) for
no-stop-signal trials (thick, solid line) and incorrect-stop-signal trials (dashed line) aligned to
saccade onset and correct-stop-signal trials (thin, solid line) aligned to the inferred time of the
commitment to stop (see Materials and Methods). B, C, Response magnitude from individual
units (points and error bars are mean ⫾ SEM) on incorrect-stop-signal versus no-stop-signal
trials, measured in the first (B) or second (C) 100 ms epoch after go/stop onset. Solid symbols
indicate that the responses were significantly different for the two conditions (Fisher’s exact
test, p ⬍ 0.01). D, E, Comparison of responses from correct-stop-signal versus no-stop-signal
trials, using the same conventions as in B and C.
⬃130 ms later (peak values of baseline-subtracted spike rates
measured between 118 and 143 ms after go-signal onset ⫽ 2.3,
2.3, 1.6, 1.5, and 2.7 spikes/s for the 5 SSDs shown in Fig. 13A–E,
respectively). In contrast, when the data are aligned to stop-signal
onset (black PETHs), there is no clear activation at the same
response latency (0.0, 0.3, 0.0, ⫺0.3, and 0.1 spikes/s in Fig.
13A–E, respectively; responses 90 –140 ms after stop-signal onset
were significantly smaller than responses in the same window
after go-signal onset for all values of SSD, Fisher’s exact test, p ⬍
0.05).
Discussion
We measured the responses of individual LC⫹ neurons in monkeys performing a simple visually guided saccade task with infrequent, randomly interleaved stop-signal trials that required the
monkeys to withhold the planned saccade. We present two novel
findings. The first is that many LC⫹ neurons exhibited phasic
activation to both sensory and motor events in the context of the
task, with an overall dominance of the motor-related activation.
The second novel finding is that LC⫹ phasic activation corresponded to the reward-driven decision to act, but not the reward-
E
Figure 13. LC⫹ activation on correct-stop-signal trials. A–E show the PETH for data combined from all single units for the indicated nominal stop-signal delay aligned to stop-signal
onset. Vertical dashed lines indicate go-signal onset. For comparison, the PETH from the same
units and the same trials, but aligned to go-signal onset (at time 0), is also shown (in gray).
driven decision not to act. Here, we discuss targeting LC⫹ and
then consider these findings in the context of previous studies of
the timing of LC phasic responses and theories of LC function.
The small size of the LC and its location deep in the brainstem
make it difficult to target for in vivo recordings, particularly in
monkeys. Histological reconstruction and clonidine sensitivity
can help but are typically used to confirm only a small subset of
recording sites in a given experiment. Instead, most sites are targeted and identified using a combination of stereotaxic coordinates and a careful characterization of the response properties of
neurons along each electrode penetration to distinguish LC from
neighboring structures. Here we reported, and confirmed via histological reconstruction, that many of the response properties
used to distinguish LC, including low baseline activity, broad
spike waveforms, and arousal sensitivity, can also be consistent
with the subC, an adjacent nucleus with NE-releasing neurons
that project to the lower brainstem and spinal cord (Westlund
and Coulter, 1980; Aston-Jones et al., 1994; Bouret and Sara,
2004; Bouret and Richmond, 2009). We do not know whether
putative LC neurons recorded in other in vivo studies in monkeys
also might have included subC units, nor were we able to distinguish LC and subC in our population of recorded units. We
suspect that a careful functional mapping of units, as shown in
Figure 3, along ML and AP axes of electrode penetrations might
be used to distinguish a medial cluster as LC and a more lateral
cluster as subC. Such AP and ML shifts can be assayed quantitatively using associated locations in the collicular saccade map
along electrode penetrations (Robinson, 1972). A careful assessment of recording locations along the depth of each track may
Kalwani et al. • Phasic Activation of Locus Ceruleus
also prove to be informative because subC lies somewhat deeper
than LC. However, we did not attempt this procedure for the
present study and therefore group all of our units as LC⫹, leaving
for future studies interesting questions about possible similarities
and differences between LC and subC activation on sensorymotor tasks.
Early studies of LC response properties emphasized the efficacy of highly arousing sensory stimuli from different modalities
in eliciting phasic LC responses (Foote et al., 1980; Aston-Jones
and Bloom, 1981b; Grant et al., 1988; Hervé-Minvielle and Sara,
1995). These responses lead to NE release throughout the brain,
including sensory areas, where it enhances the effects of incoming
signals (Abercrombie et al., 1988; Servan-Schreiber et al., 1990;
Waterhouse et al., 1998; Bouret and Sara, 2002; Devilbiss et al.,
2006; Moxon et al., 2007; Devilbiss and Waterhouse, 2011).
Other studies demonstrated that phasic LC activity depended on
the goal-directed meaning of the stimuli, but upheld the view that
these responses were correlated with the sensory events that led to
subsequent behavior (Sara and Segal, 1991; Rajkowski et al.,
2004). Therefore, the timing of LC phasic responses were typically interpreted in terms of their role in sensory processing, particularly as it related to attention and arousal (Berridge and
Waterhouse, 2003).
More recent studies have suggested a closer relationship between LC phasic activation and motor output in simple sensorymotor tasks. In monkeys performing a go/no-go task that
required them to pull a lever in response to an infrequently presented target stimulus and withhold the lever pull in response to
a nontarget stimulus, LC neurons were consistently activated by
target cues leading to a motor response. In contrast, the same LC
neurons did not respond reliably to nontarget cues, juice rewards,
or similar movements made outside of the task context. The results were interpreted in terms of a relationship between LC activation and “attended stimuli that demand a rapid motor
response” (Aston-Jones et al., 1994). In monkeys performing a
visual-discrimination task with variable RTs, LC spike timing was
more closely aligned to the motor event than the sensory event,
leading to speculation that LC responses represent the end of a
decision process that leads to a behavioral response (Clayton et
al., 2004). LC spikes were similarly timed relative to an operant
motor response on a simple conditioning task, although phasic
activation was also reported for the preceding, conditioned visual
cue that was also accompanied by Pavlovian motor responses
(Bouret and Richmond, 2009).
Despite these conflicting interpretations of LC activity in
terms of sensory or motor processing, a common finding has
been the presence of LC phasic responses only within the context
of a behavior for which the animal is rewarded. For example,
actions that activate LC neurons in the context of a task typically
do not cause comparable activations outside of the task context
(Yamamoto and Ozawa, 1989; Clayton et al., 2004). This context
dependence is thought to involve reward expectation and/or uncertainty, which is also more consistent with the decision to act
than the action itself (Sara and Segal, 1991; Aston-Jones et al.,
1994; Vankov et al., 1995; Clayton et al., 2004; Rajkowski et al.,
2004). Therefore, our finding that some LC units show phasic
activation corresponding to saccades in the intertrial interval
might still reflect task-related processing insofar as the monkeys
were trained to expect a potentially rewarding trial to begin soon
after the previous trial and thus might have been making an anticipatory movement to begin the next trial.
Our study clarifies and extends these previous findings by
demonstrating that individual LC⫹ units can be activated phasi-
J. Neurosci., October 8, 2014 • 34(41):13656 –13669 • 13667
cally in relation to both a sensory “go” signal and the immediately
resulting motor response. Our ability to distinguish these
sensory- and motor-related activations, in contrast to previous
studies, probably reflected our oculomotor task that produced
shorter, more narrowly distributed RTs than lever-release tasks
used previously (Clayton et al., 2004). We draw several conclusions from our findings, including: (1) sensory-evoked phasic
responses in LC⫹ do not require an overt motor output, because
they were present on correct-stop-signal trials; (2) at least under
our task conditions, motor-related activation typically does not
occur until after movement onset and thus occurs too late to
affect the current movement; and (3) the LC⫹ does not respond
to a either a sensory instruction or the subsequent decision not to
move even when a reward is expected, implying that reward expectation alone does not drive LC responses.
These results can be interpreted in the context of models of
countermanding. One well established class of models assumes
that there is a race between “go” and “stop” processes. Both processes are assumed to involve linear buildup signals that are triggered by the relevant sensory event and race toward a common
threshold value (Logan and Cowan, 1984; Boucher et al., 2007;
Verbruggen and Logan, 2009). The “go” rise-to-threshold process has clear correlates in the activity of particular sets of neurons
in the primate frontal eye field and superior colliculus (Hanes
and Schall, 1996; Hanes et al., 1998; Pare and Hanes, 2003). The
sensory and motor phasic LC responses that we measured on
no-stop-signal and incorrect-stop-signal trials correspond approximately to the beginning and end of this presaccadic buildup
of neural activity. The neural implementation of the stop process
is less clear, but might involve increased discharge of fixation
neurons in the frontal eye field and superior colliculus (Hanes et
al., 1998; Pare and Hanes, 2003; Boucher et al., 2007). Our results
indicate a lack of LC⫹ activation around the time of either onset
or termination of this process. Therefore, the LC⫹ is more engaged by the go than by the stop rise-to-threshold process posited
by these models.
Alternatively, a recent model suggests that instead of a separate, rise-to-threshold stop process, withholding the planned saccade involves a rapid perceptual judgment that terminates the
rise-to-threshold go process (Salinas and Stanford, 2013). This
model would imply that the task-relevant sensory and motor
phasic LC⫹ responses that we measured corresponded to the
start and threshold crossing (i.e., commitment to trigger the saccadic eye movement) of the go process on no-stop-signal and
incorrect-stop-signal trials. In contrast, the lack of responsiveness of LC⫹ neurons to stop-signal onset or the commitment to
stop on correct-stop-signal trials might reflect the fact that, according to this model, there is no rise-to threshold stop process.
More work is needed to establish more direct relationships between LC⫹ activation and the go and stop processes described by
these models.
More generally, we do not know the function of the sensoryand motor-related phasic LC⫹ activation in the context of the
countermanding task. The similarity of the sensory-related activation on all trial types, particularly when comparing correct and
incorrect-stop-signal trials (Fig. 8), suggests that, at least on a
gross level, this activation is not related to whether the monkeys
subsequently process the stop signal appropriately. Moreover,
the motor-related response occurs on both correct no-stopsignal trials and incorrect-stop-signal trials, is similar across different RTs, and typically occurs just after saccade onset. These
results imply that the subsequent NE release does not affect the
current motor response, but instead is intended to affect subse-
13668 • J. Neurosci., October 8, 2014 • 34(41):13656 –13669
quent processing, possibly in the context of evaluating the consequences of the goal-driven movement.
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